Do Multi-hop Readers Dream of Reasoning Chains?

By Haoyu Wang, Mo Yu, Xiaoxiao Guo, Rajarshi Das, Wenhan Xiong, Tian Gao. 2019

27 citations recorded by the source corpus.

General Question Answering (QA) systems over texts require the multi-hop reasoning capability, i.e. the ability to reason with information collected from multiple passages to derive the answer. In this paper we conduct a systematic analysis to assess such an ability of various existing models proposed for multi-hop QA tasks. Specifically, our analysis investigates that whether providing the full reasoning chain of multiple passages, instead of just one final passage where the answer appears, could improve the performance of the existing QA models. Surprisingly, when using the additional evidence passages, the improvements of all the existing multi-hop reading approaches are rather limited, with the highest error reduction of 5.8% on F1 (corresponding to 1.3% improvement) from the BERT model. To better understand whether the reasoning chains indeed could help find the correct answers, we further develop a co-matching-based method that leads to 13.1% error reduction with passage chains when applied to two of our base readers (including BERT). Our results demonstrate the existence of the potential improvement using explicit multi-hop reasoning and the necessity to develop models with better reasoning abilities.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Natural Language Processing, Prompting & Reasoning

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Citations in the corpus

Learning to Retrieve Reasoning Paths over Wikipedia Graph for Question Answering (2019, methodology): “…our reader jointly learns to predict the plausibility and answer the question, and moreover, fully leverages the self-attention mechanism across the concatenated paragraphs in the retrieved reasoning paths; this paragraph interaction is crucial for multi-hop reasoning (Wang et al., 2019a).”
Adaptive Information Seeking for Open-Domain Question Answering (2021, background): “In this way, the self-attention mechanism across the concatenated sequence allows each passage in the input to interact with others, which has been shown crucial for multi-hop reasoning (Wang et al., 2019a).”
From LSAT: The Progress and Challenges of Complex Reasoning (2021, background): “Constructing correct multi-hop reasoning chains (conversation flows) has been a key challenge for these tasks, and it is an essential research direction to effectively model multi-hop reasoning paths over multiple passages[40, 41].”
Teaching Algorithmic Reasoning via In-context Learning (2022, background): “It has led to improvements in performance on multi-step reasoning problems (Wang et al., 2019) such as arithmetic, commonsense, and symbolic reasoning tasks (Nye et al., 2021; Wei et al., 2022b; Lewkowycz et al., 2022a; Wang et al., 2022a,b; Anil et al., 2022; Zhou et al., 2022).”
Natural Language Inference in Context - Investigating Contextual Reasoning over Long Texts (2020, background): “• Information Integration over Paragraphs Multi-step reasoning (Liu et al. 2020b; Wang et al. 2019; Welbl, Stenetorp, and Riedel 2018) is the ability to retrieve and combine information from multiple paragraphs or multiple documents.”